This article used an open-source python repository for its analysis. It is well-suited for reproduction as more literature evolves on the intersection of urban planning and climate change. The adapted code is published alongside the article.
This article was meant to be entirely reproducible, with the data and code published alongside the article. It is however not embedded within a container (e.g. Docker). Will it past the reproducibility test tomorrow? next year? I'm curious.
The direct numerical simulations (DNS) for this paper were conducted using Basilisk (http://basilisk.fr/). As Basilisk is a free software program written in C, it can be readily installed on any Linux machine, and it should be straightforward to then run the driver code to re-produce the DNS from this paper. Given this, the numerical solutions presented in this paper are a result of many high-fidelity simulations, which each took approximately 24 CPU hours running between 4 to 8 cores. Hence the difficulty in reproducing the results should mainly be in the amount of computational resources it would take, so HPC resources will be required. The DNS in this paper were used to validate the presented analytical solutions, as well as extend the results to a longer timescale. Reproducing these numerical results will build confidence in these results, ensuring that they are independent of the system architecture they were produced on.
I tried hard to make this paper as reproducible as possible, but as techniques and dependencies become more complex, it is hard to make it 100% clear. Any form of feedback is more than welcome.
I guess it could be a cool learning experience. The paper is written with knitr, uses a seed, is part of the R package it describes, was openly written using version control (SVN, R-Forge) and is available in an open access journal (@up_jors).